Search Results for author:
Found 2 papers, 1 papers with code
Unsupervised Domain Adaptation for LiDAR Panoptic Segmentation
Unsupervised Domain Adaptation (UDA) techniques are thus essential to fill this domain gap and retain the performance of models on new sensor setups without the need for additional data labeling.
Dynamic Object Removal and Spatio-Temporal RGB-D Inpainting via Geometry-Aware Adversarial Learning
Dynamic objects have a significant impact on the robot's perception of the environment which degrades the performance of essential tasks such as localization and mapping.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
